The team proposes “OEMask-ReverseNet" [1]}, a multi-step refinement process integrating an overexposure mask. The key points are: instead of mapping RGB to RAW (Bayer pattern), the pipeline trains from RGB to demosaiced RAW [2]}; the multi-step process [3]} of this reverse ISP has greatly enhanced the performance of the baseline U-Net; the refinement pipeline can enhance other “reverse ISPs".
The method is illustrated in Figure REF : (i) unprocess the input RGB [3]} and obtain the demosaiced output [2]}; (ii) using two independent U-Net [6]} networks (U-Net(OE), U-Net(NOE)), estimate an overexposure mask; (iii) apply the mask to the previously obtained demosaiced RGB and finally mosaic it in a 4-channel (RGGB) RAW. They also propose a network to refine the predicted RAWs in the YUV space. The team implemented the method in Pytorch and trained using a combination of LPIPS [7]}, MS-SSIM-L1 and L2 loss.
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